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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
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As a Senior Associate - Machine Learning Engineer within the ASSET & WEALTH MANAGEMENT AI/ML team, you will serve as a technical individual contributor focused on designing, developing, and deploying next-generation AI/ML systems. You will build agentic AI workflows featuring multi-step reasoning, tool use, and orchestration to automate complex business processes in areas like pricing, campaign targeting, and personalization. Your daily work involves creating robust infrastructure using Python, version control, testing, and CI/CD while applying deep learning, generative AI, and LLMs to solve high-impact problems. You will also manage model validation, governance, and risk management frameworks. The role requires expertise in MLOps tooling, feature stores, and potentially causal inference methods like uplift models or causal forests to address specific challenges in customer analytics and marketing automation within the wealth management domain.
What does a Machine Learning Engineer earn?
Median $236631 from 328 postings across 54 companies.
Design and deploy end-to-end AI/ML and agentic AI systems from initial problem framing through production monitoring.
Build agentic AI workflows featuring multi-step reasoning, tool use, and orchestration to automate complex business processes.
Develop and maintain scalable ML infrastructure and pipelines using robust software engineering practices like CI/CD and modular design.
Apply deep learning, generative AI, and LLM techniques to solve problems in pricing, marketing, and customer analytics.
Implement model validation, governance, and risk-management frameworks for all models and agents in production.
Translate complex business requirements into production-grade technical solutions for both technical and non-technical stakeholders.
Establish technical standards for engineering excellence, reproducibility, and responsible AI/ML practices.
What we're looking for
At least 4 years of experience building, deploying, and scaling machine learning and AI systems in production environments.
Hands-on experience developing agentic AI systems including LLM-based agents, tool calling, multi-step reasoning, and orchestration frameworks.
Broad expertise in modern AI/ML methods, including deep learning, generative AI, LLMs, and statistical modeling.
Deep software engineering expertise in Python, including version control, testing, CI/CD, and production-grade code design.
Strong machine learning engineering skills involving ML frameworks, model pipelines, feature stores, and MLOps tooling.
Strong quantitative training in Statistics, Data Science, Computer Science, Economics, Applied Mathematics, Operations Research, or a related field.
Proven experience with model validation, diagnostic testing, and communicating technical findings to both technical and non-technical audiences.
Experience with advanced agentic AI platforms, RAG applications, causal inference methods, or relevant industry use cases (preferred); Master's or PhD degree (preferred).
Machine Learning
Python
PyTorch
C++
CUDA
Kubernetes
LLMOps
RAG
vLLM
Vector Databases
Deep Learning
Natural Language Processing
GenAI
Big Data
AWS
Agile Methodology
Model Quantization